Understanding the genetic basis of common multifactorial diseases such as cardiovascular disease (CVD) remains an elusive goal, but the great advances in molecular genetic technology, statistical genetic methods, and phenotypic assessment of CVD risk factors in recent years have facilitated more sophisticated genetic studies of risks for heart disease. The overall goal of this study is to elucidate the role of genetic factors influencing risk factors for CVD, ultimately identifying specific genes influencing the age-related progression of CVD risks. This goal will be pursued through a new and innovative collaborative research project consisting of coordinated R01 grants to Wright State University School of Medicine and the southwest Foundation for Biomedical Research. The study population centers on 764 individuals in five large, multigenerational, extended families (four white and one African-American) originally examined 25 years ago. Data collected from the original participants includes hundreds of biochemical, medical, physiological, behavioral, physical, psychological, genetic and demographic traits. To some extent, though, the original study was ahead of its time in that cost-effective whole genome mapping and statistical genetic methods for effective analysis of familial data from large extended kindred's were a decade or two away. The proposed study consists of four specific aims: 1) Collect 25-year follow-up data from approximately 500 of the original participants, and new data from approximately 500 of their relatives not examined in the original study. The CVD risk factor phenotypes to be collected include hemodynamic measures, carotid intima-media thickness, and measures of cardiopulmonary function. 2) Obtain DNA samples from these 1,000 individuals and use modern high-throughput molecular genotyping methods to create a 10 cM genetic marker map. 3) Quantify and characterize the nature of genetic influences on CVD risk factors using quantitative genetic methods suited for cross-sectional and serial (follow-up) data from relatives in large extended families. 4) Conduct linkage analyses to identify chromosomal regions (QTLs) harboring genes that influence individual variation in CVD risk factors. Following these linkage analyses, we will examine more closely our strongest linkage signals with fine mapping linkage analysis in order to narrow chromosomal regions of interest.